Diagnostic Classification of Chest X-Rays Pictures with Deep Learning Using Eye Gaze Data

Abstract

Automatic diagnosis of chest X-ray pictures with deep learning has been extensively studied in recent years. In order to improve the accuracy, it is important how to input small localized areas which are disease specific while at the same time using the information that can be obtained by the whole picture. We considered that human eye-gaze fixations can be a biomarker that indicates areas specific to disease. In this study, we propose a deep learning model utilizing eye-gaze data. We demonstrate that the classification shows the better accuracy on using eye-gaze data of experienced doctors than eye-gaze data of novice or non-use of eye-gaze information.